#setwd('/afs/inf.ed.ac.uk/user/s17/s1725186/Documents/PhD-Models/FirstPUModel/RMarkdowns')
library(tidyverse) ; library(reshape2) ; library(glue) ; library(plotly) ; library(dendextend)
library(RColorBrewer) ; library(viridis) ; require(gridExtra) ; library(GGally)
library(expss)
library(polycor)
library(foreach) ; library(doParallel)
library(knitr)
library(biomaRt)
library(anRichment) ; library(BrainDiseaseCollection)
suppressWarnings(suppressMessages(library(WGCNA)))
SFARI_colour_hue = function(r) {
pal = c('#FF7631','#FFB100','#E8E328','#8CC83F','#62CCA6','#59B9C9','#b3b3b3','#808080','gray','#d9d9d9')[r]
}
Load preprocessed dataset (preprocessing code in 20_03_02_data_preprocessing.Rmd) and clustering (pipeline in 20_03_02_WGCNA.Rmd)
# Gandal dataset
load('./../Data/preprocessed_data.RData')
datExpr = datExpr %>% data.frame
DE_info = DE_info %>% data.frame
# GO Neuronal annotations: regex 'neuron' in GO functional annotations and label the genes that make a match as neuronal
GO_annotations = read.csv('./../Data/genes_GO_annotations.csv')
GO_neuronal = GO_annotations %>% filter(grepl('neuron', go_term)) %>%
mutate('ID'=as.character(ensembl_gene_id)) %>%
dplyr::select(-ensembl_gene_id) %>% distinct(ID) %>%
mutate('Neuronal'=1)
# SFARI Genes
SFARI_genes = read_csv('./../../../SFARI/Data/SFARI_genes_08-29-2019_w_ensembl_IDs.csv')
SFARI_genes = SFARI_genes[!duplicated(SFARI_genes$ID) & !is.na(SFARI_genes$ID),]
# Clusterings
clusterings = read_csv('./../Data/clusters.csv')
# Update DE_info with SFARI and Neuronal information
genes_info = DE_info %>% mutate('ID'=rownames(.)) %>% left_join(SFARI_genes, by='ID') %>%
mutate(`gene-score`=ifelse(is.na(`gene-score`), 'None', `gene-score`)) %>%
left_join(GO_neuronal, by='ID') %>% left_join(clusterings, by='ID') %>%
mutate(Neuronal=ifelse(is.na(Neuronal), 0, Neuronal)) %>%
mutate(gene.score=ifelse(`gene-score`=='None' & Neuronal==1, 'Neuronal', `gene-score`),
significant=padj<0.05 & !is.na(padj))
# Add gene symbol
getinfo = c('ensembl_gene_id','external_gene_id')
mart = useMart(biomart='ENSEMBL_MART_ENSEMBL', dataset='hsapiens_gene_ensembl',
host='feb2014.archive.ensembl.org')
gene_names = getBM(attributes=getinfo, filters=c('ensembl_gene_id'), values=genes_info$ID, mart=mart)
genes_info = genes_info %>% left_join(gene_names, by=c('ID'='ensembl_gene_id'))
clustering_selected = 'DynamicHybridMergedSmall'
genes_info$Module = genes_info[,clustering_selected]
dataset = read.csv(paste0('./../Data/dataset_', clustering_selected, '.csv'))
dataset$Module = dataset[,clustering_selected]
rm(DE_info, GO_annotations, clusterings, getinfo, mart, dds)
Using the hetcor function, that calculates Pearson, polyserial or polychoric correlations depending on the type of variables involved.
datTraits = datMeta %>% dplyr::select(Diagnosis, Sex, Age, PMI, RNAExtractionBatch) %>%
dplyr::rename('ExtractionBatch' = RNAExtractionBatch)
# Recalculate MEs with color labels
ME_object = datExpr %>% t %>% moduleEigengenes(colors = genes_info$Module)
MEs = orderMEs(ME_object$eigengenes)
# Calculate correlation between eigengenes and the traits and their p-values
moduleTraitCor = MEs %>% apply(2, function(x) hetcor(x, datTraits)$correlations[1,-1]) %>% t
rownames(moduleTraitCor) = colnames(MEs)
colnames(moduleTraitCor) = colnames(datTraits)
moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nrow(datExpr))
# Create text matrix for the Heatmap
textMatrix = paste0(signif(moduleTraitCor, 2), ' (', signif(moduleTraitPvalue, 1), ')')
dim(textMatrix) = dim(moduleTraitCor)
# In case there are any NAs
if(sum(!complete.cases(moduleTraitCor))>0){
print(paste0(sum(is.na(moduleTraitCor)),' correlation(s) could not be calculated'))
}
## [1] "1 correlation(s) could not be calculated"
rm(ME_object)
Note: The correlations between a Modules and Diagonsis that cannot be calculated, weirdly enough, is because the initial correlation is too high, so it would be a very bad thing to lose these modules because of this numerical error. I’m going to fill in the values using the polyserial function, which doesn’t give exactly the same results as the hetcor() function, but it’s quite similar.
# Calculate the correlation tha failed with hetcor()
missing_modules = rownames(moduleTraitCor)[is.na(moduleTraitCor[,1])]
for(m in missing_modules){
cat(paste0('Correcting Module-Diagnosis correlation for Module ', m))
moduleTraitCor[m,'Diagnosis'] = polyserial(MEs[,m], datTraits$Diagnosis)
}
## Correcting Module-Diagnosis correlation for Module ME#00C1A2
## Warning in polyserial(MEs[, m], datTraits$Diagnosis): initial correlation
## inadmissible, -1.06666514307229, set to -0.9999
rm(missing_modules)
I’m going to select all the modules that have an absolute correlation higher than 0.9 with Diagnosis to study them
# Sort moduleTraitCor by Diagnosis
moduleTraitCor = moduleTraitCor[order(moduleTraitCor[,1], decreasing=TRUE),]
moduleTraitPvalue = moduleTraitPvalue[order(moduleTraitCor[,1], decreasing=TRUE),]
# Create text matrix for the Heatmap
textMatrix = paste0(signif(moduleTraitCor, 2), ' (', signif(moduleTraitPvalue, 1), ')')
dim(textMatrix) = dim(moduleTraitCor)
labeledHeatmap(Matrix = moduleTraitCor, xLabels = names(datTraits), yLabels = gsub('ME','',rownames(moduleTraitCor)),
yColorWidth=0, colors = brewer.pal(11,'PiYG'), bg.lab.y = gsub('ME','',rownames(moduleTraitCor)),
textMatrix = textMatrix, setStdMargins = FALSE, cex.text = 0.8, cex.lab.y = 0.75, zlim = c(-1,1),
main = paste('Module-Trait relationships'))
diagnosis_cor = data.frame('Module' = gsub('ME','',rownames(moduleTraitCor)),
'MTcor' = moduleTraitCor[,'Diagnosis'],
'MTpval' = moduleTraitPvalue[,'Diagnosis'])
genes_info = genes_info %>% left_join(diagnosis_cor, by='Module')
rm(moduleTraitPvalue, datTraits, textMatrix, diagnosis_cor)
The modules consist mainly of points with very high (absolute) values in PC2 (which we know is related to lfc), so this result is consistent with the high correlation between Module and Diagnosis, although some of the points with the highest PC2 values do not belong to these top modules
top_modules = gsub('ME','',rownames(moduleTraitCor)[abs(moduleTraitCor[,'Diagnosis'])>0.9])
cat(paste0('Top modules selected: ', paste(top_modules, collapse=', '),'\n'))
## Top modules selected: #F07E4E, #00C1A2
pca = datExpr %>% prcomp
plot_data = data.frame('ID'=rownames(datExpr), 'PC1' = pca$x[,1], 'PC2' = pca$x[,2]) %>%
left_join(dataset, by='ID') %>% left_join(genes_info %>% dplyr::select(ID, external_gene_id), by='ID') %>%
dplyr::select(ID, external_gene_id, PC1, PC2, Module, gene.score) %>%
mutate(ImportantModules = ifelse(Module %in% top_modules, as.character(Module), 'Others')) %>%
mutate(color = ifelse(ImportantModules=='Others','gray',ImportantModules),
alpha = ifelse(ImportantModules=='Others', 0.2, 0.4),
gene_id = paste0(ID, ' (', external_gene_id, ')'))
table(plot_data$ImportantModules)
##
## #00C1A2 #F07E4E Others
## 2399 1778 12313
ggplotly(plot_data %>% ggplot(aes(PC1, PC2, color=ImportantModules)) +
geom_point(alpha=plot_data$alpha, color=plot_data$color, aes(ID=gene_id)) + theme_minimal() +
ggtitle('Modules with strongest relation to Diagnosis'))
rm(pca)
create_plot = function(module){
plot_data = dataset %>% dplyr::select(ID, paste0('MM.',gsub('#','',module)), GS, gene.score) %>% filter(dataset$Module==module)
colnames(plot_data)[2] = 'Module'
SFARI_colors = as.numeric(names(table(as.character(plot_data$gene.score)[plot_data$gene.score!='None'])))
p = ggplotly(plot_data %>% ggplot(aes(Module, GS, color=gene.score)) + geom_point(alpha=0.5, aes(ID=ID)) + ylab('Gene Significance') +
scale_color_manual(values=SFARI_colour_hue(r=c(SFARI_colors,8))) + theme_minimal() + xlab('Module Membership') +
ggtitle(paste0('Module ', module,' (MTcor = ', round(moduleTraitCor[paste0('ME',module),1],2),')')))
return(p)
}
create_plot(top_modules[1])
create_plot(top_modules[2])
rm(create_plot)
List of top SFARI Genes in top modules ordered by SFARI score and Gene Significance
table_data = dataset %>% left_join(genes_info %>% dplyr::select(ID, external_gene_id), by='ID') %>%
dplyr::select(ID, external_gene_id, GS, gene.score, Module) %>% arrange(gene.score, desc(abs(GS))) %>%
dplyr::rename('Ensembl ID'=ID, 'Gene Symbol'=external_gene_id,
'SFARI score'=gene.score, 'Gene Significance'=GS)
kable(table_data %>% filter(Module == top_modules[1] & `SFARI score` %in% c(1,2,3)) %>% dplyr::select(-Module),
caption=paste0('Top SFARI Genes for Module ', top_modules[1]))
| Ensembl ID | Gene Symbol | Gene Significance | SFARI score |
|---|---|---|---|
| ENSG00000141431 | ASXL3 | 0.8784817 | 1 |
| ENSG00000171862 | PTEN | 0.4310990 | 1 |
| ENSG00000119335 | SET | 0.9219399 | 2 |
| ENSG00000169862 | CTNND2 | 0.8586577 | 2 |
| ENSG00000114166 | KAT2B | 0.7288804 | 2 |
| ENSG00000157103 | SLC6A1 | 0.0862649 | 2 |
| ENSG00000079482 | OPHN1 | 0.9850543 | 3 |
| ENSG00000116117 | PARD3B | 0.9746708 | 3 |
| ENSG00000109911 | ELP4 | 0.9226831 | 3 |
| ENSG00000136425 | CIB2 | 0.8720477 | 3 |
| ENSG00000164050 | PLXNB1 | 0.8656263 | 3 |
| ENSG00000112902 | SEMA5A | 0.8402428 | 3 |
| ENSG00000181722 | ZBTB20 | 0.8348181 | 3 |
| ENSG00000135387 | CAPRIN1 | 0.5999713 | 3 |
| ENSG00000089006 | SNX5 | 0.5935562 | 3 |
| ENSG00000134115 | CNTN6 | 0.5667100 | 3 |
| ENSG00000177807 | KCNJ10 | 0.5603419 | 3 |
| ENSG00000100033 | PRODH | 0.5527080 | 3 |
| ENSG00000158321 | AUTS2 | 0.5082097 | 3 |
| ENSG00000130035 | GALNT8 | 0.4868740 | 3 |
| ENSG00000128849 | CGNL1 | 0.4704090 | 3 |
| ENSG00000105379 | ETFB | 0.0270942 | 3 |
kable(table_data %>% filter(Module == top_modules[2] & `SFARI score` %in% c(1,2,3)) %>% dplyr::select(-Module),
caption=paste0('Top SFARI Genes for Module ', top_modules[2]))
| Ensembl ID | Gene Symbol | Gene Significance | SFARI score |
|---|---|---|---|
| ENSG00000136535 | TBR1 | -0.8840836 | 1 |
| ENSG00000100888 | CHD8 | -0.4787800 | 1 |
| ENSG00000171587 | DSCAM | -0.1242445 | 1 |
| ENSG00000036257 | CUL3 | 0.0279981 | 1 |
| ENSG00000174469 | CNTNAP2 | -0.9496290 | 2 |
| ENSG00000080603 | SRCAP | -0.8286620 | 2 |
| ENSG00000155974 | GRIP1 | -0.7396627 | 2 |
| ENSG00000169057 | MECP2 | -0.6872995 | 2 |
| ENSG00000105976 | MET | -0.6604760 | 2 |
| ENSG00000119866 | BCL11A | -0.6366611 | 2 |
| ENSG00000151240 | DIP2C | -0.3718812 | 2 |
| ENSG00000254585 | MAGEL2 | -0.2844695 | 2 |
| ENSG00000215301 | DDX3X | -0.1989935 | 2 |
| ENSG00000124140 | SLC12A5 | -0.9360007 | 3 |
| ENSG00000074590 | NUAK1 | -0.9347906 | 3 |
| ENSG00000078328 | RBFOX1 | -0.9186938 | 3 |
| ENSG00000144285 | SCN1A | -0.9172405 | 3 |
| ENSG00000170579 | DLGAP1 | -0.9137512 | 3 |
| ENSG00000151150 | ANK3 | -0.8831273 | 3 |
| ENSG00000183454 | GRIN2A | -0.8640121 | 3 |
| ENSG00000132294 | EFR3A | -0.8380437 | 3 |
| ENSG00000157087 | ATP2B2 | -0.8042578 | 3 |
| ENSG00000182621 | PLCB1 | -0.7901030 | 3 |
| ENSG00000197535 | MYO5A | -0.7767656 | 3 |
| ENSG00000165300 | SLITRK5 | -0.7721387 | 3 |
| ENSG00000160305 | DIP2A | -0.7005595 | 3 |
| ENSG00000176884 | GRIN1 | -0.6793649 | 3 |
| ENSG00000148737 | TCF7L2 | -0.6565504 | 3 |
| ENSG00000127616 | SMARCA4 | -0.6390948 | 3 |
| ENSG00000139174 | PRICKLE1 | -0.6154979 | 3 |
| ENSG00000146830 | GIGYF1 | -0.6088550 | 3 |
| ENSG00000175344 | CHRNA7 | -0.6003989 | 3 |
| ENSG00000170396 | ZNF804A | -0.5822747 | 3 |
| ENSG00000123552 | USP45 | -0.5491488 | 3 |
| ENSG00000169918 | OTUD7A | -0.5132753 | 3 |
| ENSG00000183495 | EP400 | -0.4976757 | 3 |
| ENSG00000170745 | KCNS3 | -0.4884112 | 3 |
| ENSG00000259207 | ITGB3 | -0.4291221 | 3 |
| ENSG00000221866 | PLXNA4 | -0.4035676 | 3 |
| ENSG00000138031 | ADCY3 | -0.3788361 | 3 |
| ENSG00000166148 | AVPR1A | -0.3650568 | 3 |
| ENSG00000103197 | TSC2 | -0.3208389 | 3 |
| ENSG00000168036 | CTNNB1 | -0.2230963 | 3 |
| ENSG00000065526 | SPEN | -0.1716413 | 3 |
| ENSG00000196876 | SCN8A | NA | 3 |
Modules with the strongest module-diagnosis correlation should have the highest percentage of SFARI Genes, but this doesn’t seem to be the case
plot_data = dataset %>% mutate('hasSFARIscore' = gene.score!='None') %>%
group_by(Module, MTcor, hasSFARIscore) %>% summarise(p=n()) %>%
left_join(dataset %>% group_by(Module) %>% summarise(n=n()), by='Module') %>%
mutate(p=round(p/n*100,2))
for(i in 1:nrow(plot_data)){
this_row = plot_data[i,]
if(this_row$hasSFARIscore==FALSE & this_row$p==100){
new_row = this_row
new_row$hasSFARIscore = TRUE
new_row$p = 0
plot_data = plot_data %>% rbind(new_row)
}
}
plot_data = plot_data %>% filter(hasSFARIscore==TRUE)
ggplotly(plot_data %>% ggplot(aes(MTcor, p, size=n)) + geom_smooth(color='gray', se=FALSE) +
geom_point(color=plot_data$Module, alpha=0.5, aes(id=Module)) + geom_hline(yintercept=mean(plot_data$p), color='gray') +
xlab('Module-Diagnosis correlation') + ylab('% of SFARI genes') +
theme_minimal() + theme(legend.position = 'none'))
rm(i, this_row, new_row, plot_data)
Breaking the SFARI genes by score
scores = c(1,2,3,4,5,6,'None')
plot_data = dataset %>% group_by(Module, MTcor, gene.score) %>% summarise(n=n()) %>%
left_join(dataset %>% group_by(Module) %>% summarise(N=n()), by='Module') %>%
mutate(p=round(n/N*100,2), gene.score = as.character(gene.score))
for(i in 1:nrow(plot_data)){
this_row = plot_data[i,]
if(sum(plot_data$Module == this_row$Module)<7){
missing_scores = which(! scores %in% plot_data$gene.score[plot_data$Module == this_row$Module])
for(s in missing_scores){
new_row = this_row
new_row$gene.score = as.character(s)
new_row$n = 0
new_row$p = 0
plot_data = plot_data %>% rbind(new_row)
}
}
}
plot_data = plot_data %>% filter(gene.score != 'None')
plot_function = function(i){
i = 2*i-1
plot_list = list()
for(j in 1:2){
plot_list[[j]] = ggplotly(plot_data %>% filter(gene.score==scores[i+j-1]) %>% ggplot(aes(MTcor, p, size=n)) +
geom_smooth(color='gray', se=FALSE) +
geom_point(color=plot_data$Module[plot_data$gene.score==scores[i+j-1]], alpha=0.5, aes(id=Module)) +
geom_hline(yintercept=mean(plot_data$p[plot_data$gene.score==scores[i+j-1]]), color='gray') +
xlab('Module-Diagnosis correlation') + ylab('% of SFARI genes') +
theme_minimal() + theme(legend.position = 'none'))
}
p = subplot(plot_list, nrows=1) %>% layout(annotations = list(
list(x = 0.2 , y = 1.05, text = paste0('SFARI score ', scores[i]), showarrow = F, xref='paper', yref='paper'),
list(x = 0.8 , y = 1.05, text = paste0('SFARI score ', scores[i+1]), showarrow = F, xref='paper', yref='paper')))
return(p)
}
plot_function(1)
plot_function(2)
plot_function(3)
rm(i, s, this_row, new_row, plot_function)
Since these modules have the strongest relation to autism, this pattern should be reflected in their model eigengenes, having two different behaviours for the samples corresponding to autism and the ones corresponding to control.
In both cases, the Eigengenes separate the behaviour between autism and control samples very clearly!
plot_EGs = function(module){
plot_data = data.frame('ID' = rownames(MEs), 'MEs' = MEs[,paste0('ME',module)], 'Diagnosis' = datMeta$Diagnosis)
p = plot_data %>% ggplot(aes(Diagnosis, MEs, fill=Diagnosis)) + geom_boxplot() + theme_minimal() + theme(legend.position='none') +
ggtitle(paste0('Module ', module, ' (MTcor=',round(moduleTraitCor[paste0('ME',module),1],2),')'))
return(p)
}
p1 = plot_EGs(top_modules[1])
p2 = plot_EGs(top_modules[2])
grid.arrange(p1, p2, nrow=1)
rm(plot_EGs, p1, p2)
Selecting the modules with the highest correlation to Diagnosis, and, from them, the genes with the highest module membership-(absolute) gene significance
*Ordered by \(\frac{MM+|GS|}{2}\)
There aren’t many SFARI genes in the top genes of each module, and not a single SFARI score 1 or 2
create_table = function(module){
top_genes = dataset %>% left_join(genes_info %>% dplyr::select(ID, external_gene_id), by='ID') %>%
dplyr::select(ID, external_gene_id, paste0('MM.',gsub('#','',module)), GS, gene.score) %>%
filter(dataset$Module==module) %>% dplyr::rename('MM' = paste0('MM.',gsub('#','',module))) %>%
mutate(importance = (MM+abs(GS))/2) %>% arrange(by=-importance) %>% top_n(20)
return(top_genes)
}
top_genes = list()
for(i in 1:length(top_modules)) top_genes[[i]] = create_table(top_modules[i])
kable(top_genes[[1]], caption=paste0('Top 10 genes for module ', top_modules[1], ' (MTcor = ',
round(moduleTraitCor[paste0('ME',top_modules[1]),1],2),')'))
| ID | external_gene_id | MM | GS | gene.score | importance |
|---|---|---|---|---|---|
| ENSG00000007372 | PAX6 | 0.9447999 | 0.9850212 | None | 0.9649106 |
| ENSG00000145555 | MYO10 | 0.9501165 | 0.9760837 | None | 0.9631001 |
| ENSG00000172380 | GNG12 | 0.9060339 | 0.9999000 | None | 0.9529670 |
| ENSG00000144369 | FAM171B | 0.8982286 | 0.9999000 | None | 0.9490643 |
| ENSG00000163110 | PDLIM5 | 0.9520725 | 0.9439184 | None | 0.9479954 |
| ENSG00000079482 | OPHN1 | 0.9105460 | 0.9850543 | 3 | 0.9478002 |
| ENSG00000143171 | RXRG | 0.8976355 | 0.9979414 | None | 0.9477885 |
| ENSG00000116117 | PARD3B | 0.9143320 | 0.9746708 | 3 | 0.9445014 |
| ENSG00000187398 | LUZP2 | 0.8907042 | 0.9977362 | None | 0.9442202 |
| ENSG00000103876 | FAH | 0.8883935 | 0.9999000 | None | 0.9441468 |
| ENSG00000101400 | SNTA1 | 0.9145689 | 0.9669187 | None | 0.9407438 |
| ENSG00000154319 | FAM167A | 0.9432367 | 0.9379285 | None | 0.9405826 |
| ENSG00000109472 | CPE | 0.9155055 | 0.9620980 | None | 0.9388018 |
| ENSG00000164199 | GPR98 | 0.8751852 | 0.9999000 | None | 0.9375426 |
| ENSG00000092820 | EZR | 0.9399859 | 0.9295938 | None | 0.9347898 |
| ENSG00000178252 | WDR6 | 0.8900780 | 0.9736793 | None | 0.9318787 |
| ENSG00000181449 | SOX2 | 0.9248853 | 0.9345981 | None | 0.9297417 |
| ENSG00000153208 | MERTK | 0.8729992 | 0.9862742 | None | 0.9296367 |
| ENSG00000181467 | RAP2B | 0.8579352 | 0.9999000 | None | 0.9289176 |
| ENSG00000148498 | PARD3 | 0.9194409 | 0.9330113 | None | 0.9262261 |
kable(top_genes[[2]], caption=paste0('Top 10 genes for module ', top_modules[2], ' (MTcor = ',
round(moduleTraitCor[paste0('ME',top_modules[2]),1],2),')'))
| ID | external_gene_id | MM | GS | gene.score | importance |
|---|---|---|---|---|---|
| ENSG00000215397 | SCRT2 | 0.9310149 | -0.9999000 | None | 0.9654575 |
| ENSG00000051382 | PIK3CB | 0.9154676 | -0.9940774 | None | 0.9547725 |
| ENSG00000248905 | FMN1 | 0.9084369 | -0.9999000 | None | 0.9541684 |
| ENSG00000162694 | EXTL2 | 0.9222451 | -0.9840084 | None | 0.9531267 |
| ENSG00000188582 | PAQR9 | 0.9137114 | -0.9915218 | None | 0.9526166 |
| ENSG00000163577 | EIF5A2 | 0.9196400 | -0.9774397 | None | 0.9485399 |
| ENSG00000150967 | ABCB9 | 0.9003595 | -0.9922264 | None | 0.9462929 |
| ENSG00000148798 | INA | 0.8879031 | -0.9999000 | None | 0.9439015 |
| ENSG00000159840 | ZYX | 0.8902263 | -0.9920783 | None | 0.9411523 |
| ENSG00000141314 | RHBDL3 | 0.8915731 | -0.9902975 | None | 0.9409353 |
| ENSG00000078328 | RBFOX1 | 0.9611403 | -0.9186938 | 3 | 0.9399171 |
| ENSG00000118596 | SLC16A7 | 0.9641080 | -0.9141944 | 5 | 0.9391512 |
| ENSG00000170049 | KCNAB3 | 0.9071309 | -0.9706140 | None | 0.9388725 |
| ENSG00000163624 | CDS1 | 0.8765489 | -0.9999000 | None | 0.9382244 |
| ENSG00000138442 | WDR12 | 0.9051402 | -0.9675778 | None | 0.9363590 |
| ENSG00000164588 | HCN1 | 0.9057786 | -0.9653795 | None | 0.9355790 |
| ENSG00000171004 | HS6ST2 | 0.9028105 | -0.9664944 | None | 0.9346524 |
| ENSG00000164114 | MAP9 | 0.8751424 | -0.9941484 | None | 0.9346454 |
| ENSG00000144285 | SCN1A | 0.9437904 | -0.9172405 | 3 | 0.9305154 |
| ENSG00000155886 | SLC24A2 | 0.9396361 | -0.9212809 | 4 | 0.9304585 |
rm(create_table)
pca = datExpr %>% prcomp
ids = c()
for(tg in top_genes) ids = c(ids, tg$ID)
plot_data = data.frame('ID'=rownames(datExpr), 'PC1' = pca$x[,1], 'PC2' = pca$x[,2]) %>%
left_join(dataset, by='ID') %>% dplyr::select(ID, PC1, PC2, Module, gene.score) %>%
mutate(color = ifelse(Module %in% top_modules, as.character(Module), 'gray')) %>%
mutate(alpha = ifelse(color %in% top_modules &
ID %in% ids, 1, 0.1))
plot_data %>% ggplot(aes(PC1, PC2)) + geom_point(alpha=plot_data$alpha, color=plot_data$color) +
theme_minimal() + ggtitle('Important genes identified through WGCNA')
Level of expression by Diagnosis for top genes, ordered by importance (defined above)
create_plot = function(i){
plot_data = datExpr[rownames(datExpr) %in% top_genes[[i]]$ID,] %>% mutate('ID' = rownames(.)) %>%
melt(id.vars='ID') %>% mutate(variable = gsub('X','',variable)) %>%
left_join(top_genes[[i]], by='ID') %>%
left_join(datMeta %>% dplyr::select(Dissected_Sample_ID, Diagnosis),
by = c('variable'='Dissected_Sample_ID')) %>% arrange(desc(importance))
p = ggplotly(plot_data %>% mutate(external_gene_id=factor(external_gene_id,
levels=unique(plot_data$external_gene_id), ordered=T)) %>%
ggplot(aes(external_gene_id, value, fill=Diagnosis)) + geom_boxplot() + theme_minimal() +
xlab(paste0('Top genes for module ', top_modules[i], ' (MTcor = ',
round(genes_info$MTcor[genes_info$Module==top_modules[i]][1],2), ')')) + ylab('Level of Expression') +
theme(axis.text.x = element_text(angle = 90, hjust = 1)))
return(p)
}
create_plot(1)
create_plot(2)
rm(create_plot)
Using the package anRichment
It was designed by Peter Langfelder explicitly to perform enrichmen analysis on WGCNA’s modules in brain-related experiments (mainly Huntington’s Disease)
It has packages with brain annotations:
BrainDiseaseCollection: A Brain Disease Gene Set Collection for anRichment
MillerAIBSCollection: (included in anRichment) Contains gene sets collected by Jeremy A. Miller at AIBS of various cell type and brain region marker sets, gene sets collected from expression studies of developing brain, as well as a collection of transcription factor (TF) targets from the original ChEA study
The tutorial says it’s an experimental package
It’s not on CRAN nor in Bioconductor
# Prepare dataset
# Create dataset with top modules membership and removing the genes without an assigned module
EA_dataset = data.frame('ensembl_gene_id' = genes_info$ID,
module = ifelse(genes_info$Module %in% top_modules, genes_info$Module, 'other')) %>%
filter(genes_info$Module!='gray')
# Assign Entrez Gene Id to each gene
getinfo = c('ensembl_gene_id','entrezgene')
mart = useMart(biomart='ENSEMBL_MART_ENSEMBL', dataset='hsapiens_gene_ensembl', host='feb2014.archive.ensembl.org')
biomart_output = getBM(attributes=getinfo, filters=c('ensembl_gene_id'), values=EA_dataset$ensembl_gene_id, mart=mart)
## Cache found
EA_dataset = EA_dataset %>% left_join(biomart_output, by='ensembl_gene_id')
for(tm in top_modules){
cat(paste0('\n',sum(EA_dataset$module==tm & is.na(EA_dataset$entrezgene)), ' genes from top module ',
tm, ' don\'t have an Entrez Gene ID'))
}
##
## 27 genes from top module #F07E4E don't have an Entrez Gene ID
## 45 genes from top module #00C1A2 don't have an Entrez Gene ID
rm(getinfo, mart, biomart_output, tm)
# Manual: https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/GeneAnnotation/Tutorials/anRichment-Tutorial1.pdf
collectGarbage()
# EA_dataset = rbind(EA_dataset[EA_dataset$module!='other',], EA_dataset[EA_dataset$module=='other',][sample(sum(EA_dataset$module=='other'), 1000),])
# Prepare datasets
GO_col = buildGOcollection(organism = 'human', verbose = 0)
## Loading required package: org.Hs.eg.db
##
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
internal_col = internalCollection(organism = 'human')
MillerAIBS_col = MillerAIBSCollection(organism = 'human')
BrainDisease_col = BrainDiseaseCollection(organism = 'human')
combined_col = mergeCollections(GO_col, internal_col, MillerAIBS_col, BrainDisease_col)
# Print collections used
cat('Using collections: ')
## Using collections:
knownGroups(combined_col, sortBy = 'size')
## [1] "GO"
## [2] "GO.BP"
## [3] "GO.MF"
## [4] "GO.CC"
## [5] "JA Miller at AIBS"
## [6] "Chip-X enrichment analysis (ChEA)"
## [7] "Brain"
## [8] "JAM"
## [9] "Prenatal brain"
## [10] "Brain region markers"
## [11] "Cortex"
## [12] "Brain region marker enriched gene sets"
## [13] "WGCNA"
## [14] "BrainRegionMarkers"
## [15] "BrainRegionMarkers.HBA"
## [16] "BrainRegionMarkers.HBA.localMarker(top200)"
## [17] "Postnatal brain"
## [18] "ImmunePathways"
## [19] "Markers of cortex layers"
## [20] "BrainLists"
## [21] "Cell type markers"
## [22] "Germinal brain"
## [23] "BrainRegionMarkers.HBA.globalMarker(top200)"
## [24] "Accelerated evolution"
## [25] "Postmitotic brain"
## [26] "BrainLists.Blalock_AD"
## [27] "BrainLists.DiseaseGenes"
## [28] "BloodAtlases"
## [29] "Verge Disease Genes"
## [30] "BloodAtlases.Whitney"
## [31] "BrainLists.JAXdiseaseGene"
## [32] "BrainLists.MO"
## [33] "Age-associated genes"
## [34] "BrainLists.Lu_Aging"
## [35] "Cell type marker enriched gene sets"
## [36] "BrainLists.CA1vsCA3"
## [37] "BrainLists.MitochondrialType"
## [38] "BrainLists.MO.2+_26Mar08"
## [39] "BrainLists.MO.Sugino"
## [40] "BloodAtlases.Gnatenko2"
## [41] "BloodAtlases.Kabanova"
## [42] "BrainLists.Voineagu"
## [43] "StemCellLists"
## [44] "StemCellLists.Lee"
# Perform Enrichment Analysis
enrichment = enrichmentAnalysis(classLabels = EA_dataset$module, identifiers = EA_dataset$entrezgene,
refCollection = combined_col, useBackground = 'given',
threshold = 1e-4, thresholdType = 'Bonferroni',
getOverlapEntrez = FALSE, getOverlapSymbols = TRUE)
## enrichmentAnalysis: preparing data..
## ..working on label set 1 ..
kable(enrichment$enrichmentTable %>% filter(class==top_modules[1]) %>%
dplyr::select(dataSetID, shortDataSetName, inGroups, Bonferroni, FDR, enrichmentRatio,
effectiveClassSize, effectiveSetSize, nCommonGenes) %>%
arrange(Bonferroni, desc(enrichmentRatio)),
caption = paste0('Enriched terms for module ', top_modules[1], ' (MTcor = ',
round(genes_info$MTcor[genes_info$Module==top_modules[1]][1],4), ')'))
| dataSetID | shortDataSetName | inGroups | Bonferroni | FDR | enrichmentRatio | effectiveClassSize | effectiveSetSize | nCommonGenes |
|---|---|---|---|---|---|---|---|---|
| JAMiller.AIBS.000097 | Cortical astrocytes | JA Miller at AIBS|Brain|Postnatal brain|Cell type markers|Cortex | 0.00e+00 | 0.0e+00 | 1.960997 | 1806 | 2282 | 494 |
| JAMiller.AIBS.000085 | Cerebellar astrocytes | JA Miller at AIBS|Brain|Postnatal brain|Cell type markers | 0.00e+00 | 0.0e+00 | 2.291653 | 1806 | 1427 | 361 |
| JAMiller.AIBS.000084 | Bergman glia | JA Miller at AIBS|Brain|Postnatal brain|Cell type markers | 0.00e+00 | 0.0e+00 | 2.405162 | 1806 | 1209 | 321 |
| JAMiller.AIBS.000143 | Lowest in CP of 13-16 post-conception weeks human | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex | 0.00e+00 | 0.0e+00 | 2.214066 | 1806 | 1432 | 350 |
| JAMiller.AIBS.000112 | HippocampusWGCNA greenyellow SGZenriched astrocyte | JA Miller at AIBS|Brain|Postnatal brain|WGCNA | 0.00e+00 | 0.0e+00 | 3.951529 | 1806 | 243 | 106 |
| JAMiller.AIBS.000009 | VZ markers at 15-16 post-conception weeks | JA Miller at AIBS|Brain|Prenatal brain|Cortex|Markers of cortex layers|Germinal brain | 0.00e+00 | 0.0e+00 | 2.093859 | 1806 | 1233 | 285 |
| JAM:002773 | upAging_copperHomeostatisMT1 | JAM|BrainLists|BrainLists.Lu_Aging | 0.00e+00 | 0.0e+00 | 5.032607 | 1806 | 117 | 65 |
| JAMiller.AIBS.000148 | Highest in VZ of 13-16 post-conception weeks human | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex|Germinal brain | 0.00e+00 | 0.0e+00 | 2.417462 | 1806 | 667 | 178 |
| JAMiller.AIBS.000098 | Cortical oligodendrocytes (Olig2) | JA Miller at AIBS|Brain|Postnatal brain|Cell type markers|Cortex | 0.00e+00 | 0.0e+00 | 1.622513 | 1806 | 2250 | 403 |
| JAMiller.AIBS.000154 | Highest in VZ of E14.5 mouse | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex|Germinal brain | 0.00e+00 | 0.0e+00 | 1.689180 | 1806 | 1700 | 317 |
| JAMiller.AIBS.000204 | RegionalWGCNA midfetal M34 | JA Miller at AIBS|Brain|Prenatal brain|WGCNA | 0.00e+00 | 0.0e+00 | 2.438879 | 1806 | 416 | 112 |
| JAMiller.AIBS.000002 | SZo markers at 21 post-conception weeks | JA Miller at AIBS|Brain|Prenatal brain|Cortex|Markers of cortex layers|Germinal brain | 0.00e+00 | 0.0e+00 | 2.851142 | 1806 | 251 | 79 |
| JAMiller.AIBS.000110 | HippocampusWGCNA cyan SGZenriched upAge glia/gliogenesis | JA Miller at AIBS|Brain|Postnatal brain|WGCNA | 0.00e+00 | 0.0e+00 | 3.488821 | 1806 | 148 | 57 |
| JAMiller.AIBS.000193 | CortexWGCNA midfetal M23 | JA Miller at AIBS|Brain|Prenatal brain|WGCNA | 0.00e+00 | 0.0e+00 | 1.785563 | 1806 | 969 | 191 |
| JAMiller.AIBS.000106 | Genes enriched in the hippocampal SGZ in mouse | JA Miller at AIBS|Brain|Postnatal brain|Markers of cortex layers | 0.00e+00 | 0.0e+00 | 2.465516 | 1806 | 327 | 89 |
| JAMiller.AIBS.000062 | CortexWGCNA 15-21 post-conception weeks C36 SZ/VZenriched | JA Miller at AIBS|Brain|Prenatal brain|Cortex|WGCNA | 0.00e+00 | 0.0e+00 | 3.218220 | 1806 | 152 | 54 |
| JAMiller.AIBS.000064 | CortexWGCNA 15-21 post-conception weeks C38 cellCycle SZ/VZenriched | JA Miller at AIBS|Brain|Prenatal brain|Cortex|WGCNA | 0.00e+00 | 0.0e+00 | 2.066622 | 1806 | 526 | 120 |
| JAMiller.AIBS.000075 | GerminalZonesWGCNA 15-21 post-conception weeks G7 VZ-enriched | JA Miller at AIBS|Brain|Prenatal brain|Cortex|WGCNA|Germinal brain | 0.00e+00 | 0.0e+00 | 1.916550 | 1806 | 605 | 128 |
| JAMiller.AIBS.000082 | Cerebellar oligodendrocytes (Olig2) | JA Miller at AIBS|Brain|Postnatal brain|Cell type markers | 1.00e-07 | 0.0e+00 | 1.528120 | 1806 | 1482 | 250 |
| JAMiller.AIBS.000138 | VZ/SZ/IZ enriched in E14.5 mouse | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex|Germinal brain | 1.00e-07 | 0.0e+00 | 2.237388 | 1806 | 332 | 82 |
| JAMiller.AIBS.000126 | Subependymal zone parenchymalAstrocytesFromDiencephalon(GFAP+/inDiencephalon) | JA Miller at AIBS|Brain|Postnatal brain|Cell type markers | 3.00e-07 | 0.0e+00 | 3.301767 | 1806 | 107 | 39 |
| JAMiller.AIBS.000137 | VZ enriched in E14.5 mouse | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex|Germinal brain | 1.10e-06 | 0.0e+00 | 1.829736 | 1806 | 604 | 122 |
| JAM:003117 | noChangeAD_antigenProcessing_ribosome | JAM|BrainLists|BrainLists.Blalock_AD | 1.30e-06 | 0.0e+00 | 2.385829 | 1806 | 243 | 64 |
| JAMiller.AIBS.000000 | VZ markers at 21 post-conception weeks | JA Miller at AIBS|Brain|Prenatal brain|Cortex|Markers of cortex layers|Germinal brain | 2.50e-06 | 1.0e-07 | 2.616956 | 1806 | 180 | 52 |
| JAMiller.AIBS.000163 | Genes decreasing in fetal and increasing in aging | JA Miller at AIBS|Brain|Age-associated genes|Cortex | 3.10e-06 | 1.0e-07 | 3.902206 | 1806 | 65 | 28 |
| GO:0005576 | extracellular region | GO|GO.CC | 6.40e-06 | 2.0e-07 | 1.293701 | 1806 | 3249 | 464 |
| GO:0044421 | extracellular region part | GO|GO.CC | 2.93e-05 | 8.0e-07 | 1.317506 | 1806 | 2709 | 394 |
| JAMiller.AIBS.000116 | HippocampusWGCNA midnightblue SGZenriched | JA Miller at AIBS|Brain|Postnatal brain|WGCNA | 6.93e-05 | 1.8e-06 | 2.596441 | 1806 | 157 | 45 |
kable(enrichment$enrichmentTable %>% filter(class==top_modules[2]) %>%
dplyr::select(dataSetID, shortDataSetName, inGroups, Bonferroni, FDR, enrichmentRatio,
effectiveClassSize, effectiveSetSize, nCommonGenes) %>%
arrange(Bonferroni, desc(enrichmentRatio)),
caption = paste0('Enriched terms for module ', top_modules[2], ' (MTcor = ',
round(genes_info$MTcor[genes_info$Module==top_modules[2]][1],4), ')'))
| dataSetID | shortDataSetName | inGroups | Bonferroni | FDR | enrichmentRatio | effectiveClassSize | effectiveSetSize | nCommonGenes |
|---|---|---|---|---|---|---|---|---|
| JAM:002967 | Occipital Lobe_IN_Cerebral Cortex | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.0e+00 | 0e+00 | 3.760216 | 2414 | 155 | 86 |
| JAM:002744 | Autism_differential_expression_across_at_least_one_comparison | JAM|BrainLists|BrainLists.Voineagu | 0.0e+00 | 0e+00 | 1.914495 | 2414 | 754 | 213 |
| JAM:002985 | Parietal Lobe_IN_Cerebral Cortex | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.0e+00 | 0e+00 | 3.273700 | 2414 | 118 | 57 |
| JAM:002824 | Dentate Nucleus_IN_Cerebellar Nucleus | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.0e+00 | 0e+00 | 2.862392 | 2414 | 161 | 68 |
| JAMiller.AIBS.000142 | Highest in CP of 13-16 post-conception weeks human | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex|Postmitotic brain | 0.0e+00 | 0e+00 | 1.575287 | 2414 | 1196 | 278 |
| JAM:002805 | Cerebral Cortex | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.globalMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.0e+00 | 0e+00 | 2.728456 | 2414 | 154 | 62 |
| JAM:002986 | parietal part, inferior bank of gyrus_IN_Cingulate Gyrus | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.0e+00 | 0e+00 | 2.961436 | 2414 | 119 | 52 |
| JAMiller.AIBS.000134 | Layer4 enriched in adult macaque cortex | JA Miller at AIBS|Brain|Postnatal brain|Markers of cortex layers|Cortex | 1.0e-07 | 0e+00 | 2.671279 | 2414 | 137 | 54 |
| JAMiller.AIBS.000155 | Lowest in VZ of E14.5 mouse | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex | 4.4e-06 | 1e-07 | 1.378540 | 2414 | 1642 | 334 |
| JAM:003072 | Tail of Caudate Nucleus_IN_Striatum | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 9.2e-06 | 3e-07 | 2.371997 | 2414 | 160 | 56 |
Save Enrichment Analysis results
save(enrichment, file='./../Data/enrichmentAnalysis.RData')
#load('./../Data/enrichmentAnalysis.RData')
sessionInfo()
## R version 3.6.0 (2019-04-26)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: Scientific Linux 7.6 (Nitrogen)
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
##
## locale:
## [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
## [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
## [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] org.Hs.eg.db_3.10.0
## [2] BrainDiseaseCollection_1.00
## [3] anRichment_1.01-2
## [4] TxDb.Mmusculus.UCSC.mm10.knownGene_3.10.0
## [5] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [6] GenomicFeatures_1.38.2
## [7] GenomicRanges_1.38.0
## [8] GenomeInfoDb_1.22.0
## [9] anRichmentMethods_0.90-1
## [10] WGCNA_1.68
## [11] fastcluster_1.1.25
## [12] dynamicTreeCut_1.63-1
## [13] GO.db_3.10.0
## [14] AnnotationDbi_1.48.0
## [15] IRanges_2.20.2
## [16] S4Vectors_0.24.3
## [17] Biobase_2.46.0
## [18] BiocGenerics_0.32.0
## [19] biomaRt_2.42.0
## [20] knitr_1.24
## [21] doParallel_1.0.15
## [22] iterators_1.0.12
## [23] foreach_1.4.7
## [24] polycor_0.7-10
## [25] expss_0.10.1
## [26] GGally_1.4.0
## [27] gridExtra_2.3
## [28] viridis_0.5.1
## [29] viridisLite_0.3.0
## [30] RColorBrewer_1.1-2
## [31] dendextend_1.13.3
## [32] plotly_4.9.2
## [33] glue_1.3.1
## [34] reshape2_1.4.3
## [35] forcats_0.4.0
## [36] stringr_1.4.0
## [37] dplyr_0.8.3
## [38] purrr_0.3.3
## [39] readr_1.3.1
## [40] tidyr_1.0.2
## [41] tibble_2.1.3
## [42] ggplot2_3.2.1
## [43] tidyverse_1.3.0
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.1.5
## [3] Hmisc_4.2-0 BiocFileCache_1.10.2
## [5] plyr_1.8.5 lazyeval_0.2.2
## [7] splines_3.6.0 crosstalk_1.0.0
## [9] BiocParallel_1.20.1 robust_0.4-18.2
## [11] digest_0.6.24 htmltools_0.4.0
## [13] fansi_0.4.1 magrittr_1.5
## [15] checkmate_1.9.4 memoise_1.1.0
## [17] fit.models_0.5-14 cluster_2.0.8
## [19] annotate_1.64.0 Biostrings_2.54.0
## [21] modelr_0.1.5 matrixStats_0.55.0
## [23] askpass_1.1 prettyunits_1.0.2
## [25] colorspace_1.4-1 blob_1.2.1
## [27] rvest_0.3.5 rappdirs_0.3.1
## [29] rrcov_1.4-7 haven_2.2.0
## [31] xfun_0.8 crayon_1.3.4
## [33] RCurl_1.95-4.12 jsonlite_1.6
## [35] genefilter_1.68.0 impute_1.60.0
## [37] survival_2.44-1.1 gtable_0.3.0
## [39] zlibbioc_1.32.0 XVector_0.26.0
## [41] DelayedArray_0.12.2 DEoptimR_1.0-8
## [43] scales_1.1.0 mvtnorm_1.0-11
## [45] DBI_1.1.0 Rcpp_1.0.3
## [47] xtable_1.8-4 progress_1.2.2
## [49] htmlTable_1.13.1 foreign_0.8-71
## [51] bit_1.1-15.2 preprocessCore_1.48.0
## [53] Formula_1.2-3 htmlwidgets_1.5.1
## [55] httr_1.4.1 ellipsis_0.3.0
## [57] acepack_1.4.1 farver_2.0.3
## [59] pkgconfig_2.0.3 reshape_0.8.8
## [61] XML_3.99-0.3 nnet_7.3-12
## [63] dbplyr_1.4.2 locfit_1.5-9.1
## [65] later_1.0.0 labeling_0.3
## [67] tidyselect_0.2.5 rlang_0.4.4
## [69] munsell_0.5.0 cellranger_1.1.0
## [71] tools_3.6.0 cli_2.0.1
## [73] generics_0.0.2 RSQLite_2.2.0
## [75] broom_0.5.4 fastmap_1.0.1
## [77] evaluate_0.14 yaml_2.2.0
## [79] bit64_0.9-7 fs_1.3.1
## [81] robustbase_0.93-5 nlme_3.1-139
## [83] mime_0.9 xml2_1.2.2
## [85] compiler_3.6.0 rstudioapi_0.10
## [87] curl_4.3 reprex_0.3.0
## [89] geneplotter_1.64.0 pcaPP_1.9-73
## [91] stringi_1.4.6 highr_0.8
## [93] lattice_0.20-38 Matrix_1.2-17
## [95] vctrs_0.2.2 pillar_1.4.3
## [97] lifecycle_0.1.0 data.table_1.12.8
## [99] bitops_1.0-6 httpuv_1.5.2
## [101] rtracklayer_1.46.0 R6_2.4.1
## [103] latticeExtra_0.6-28 promises_1.1.0
## [105] codetools_0.2-16 MASS_7.3-51.4
## [107] assertthat_0.2.1 SummarizedExperiment_1.16.1
## [109] DESeq2_1.26.0 openssl_1.4.1
## [111] withr_2.1.2 GenomicAlignments_1.22.1
## [113] Rsamtools_2.2.2 GenomeInfoDbData_1.2.2
## [115] hms_0.5.3 grid_3.6.0
## [117] rpart_4.1-15 rmarkdown_1.14
## [119] Cairo_1.5-10 shiny_1.4.0
## [121] lubridate_1.7.4 base64enc_0.1-3